Growing awareness of wellness has prompted people to consider whether their dietary patterns align with their health and fitness goals. In response, researchers have introduced various wearable dietary monitoring systems and dietary assessment approaches. However, these solutions are either limited to identifying foods with simple ingredients or insufficient in providing an analysis of individual dietary behaviors with domain-specific knowledge. In this paper, we present DietGlance, a system that automatically monitors dietary behaviors in daily routines and delivers personalized analysis from knowledge sources. DietGlance first detects ingestive episodes from multimodal inputs using eyeglasses, capturing privacy-preserving meal images of various dishes being consumed. Based on the inferred food items and consumed quantities from these images, DietGlance further provides nutritional analysis and personalized dietary suggestions, empowered by the retrieval-augmented generation module on a reliable nutrition library. A short-term user study (N=33) and a four-week longitudinal study (N=16) demonstrate the usability and effectiveness of DietGlance, offering insights and implications for future AI-assisted dietary monitoring and personalized healthcare intervention systems using eyewear.

翻译：随着健康意识的日益增强，人们开始关注自身饮食模式是否符合其健康与健身目标。为此，研究人员已提出多种可穿戴饮食监测系统与饮食评估方法。然而，现有方案或仅限于识别成分简单的食物，或缺乏结合领域专业知识对个体饮食行为进行分析的能力。本文提出DietGlance系统，该系统能够自动监测日常饮食行为，并基于知识源提供个性化分析。DietGlance首先通过智能眼镜的多模态输入检测摄食行为，以保护隐私的方式捕捉食用各类菜肴的餐食图像。基于从图像中推断出的食物种类与摄入量，系统进一步通过检索增强生成模块调用可靠营养数据库，提供营养分析与个性化饮食建议。一项短期用户研究（N=33）与为期四周的纵向研究（N=16）验证了DietGlance的可用性与有效性，为未来基于眼镜设备的AI辅助饮食监测及个性化健康干预系统提供了启示与参考。